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Ecological modularity as a means to reduce necessary training environments in evolutionary robotics.

, , and . GECCO (Companion), page 53-54. ACM, (2017)

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Evolving morphology automatically reformulates the problem of designing modular control., and . Adapt. Behav., 26 (2): 47-64 (2018)Improving Robot Behavior Optimization by Combining User Preferences., , and . ALIFE, page 973-980. MIT Press, (2014)Morphological Modularity Can Enable the Evolution of Robot Behavior to Scale Linearly with the Number of Environmental Features., , , , and . Frontiers Robotics AI, (2016)Evolving Robot Morphology Facilitates the Evolution of Neural Modularity and Evolvability., , , , , and . GECCO, page 129-136. ACM, (2015)Exploiting the Relationship Between Structural Modularity and Sparsity for Faster Network Evolution., and . GECCO (Companion), page 1173-1176. ACM, (2015)Embodiment Effects in Evolutionary Robotics., , , , , , , , and . CogSci, cognitivesciencesociety.org, (2016)Ecological modularity as a means to reduce necessary training environments in evolutionary robotics., , and . GECCO (Companion), page 53-54. ACM, (2017)Simulating the evolution of soft and rigid-body robots., , , , , and . GECCO (Companion), page 1117-1120. ACM, (2017)Modularity and Sparsity: Evolution of Neural Net Controllers in Physically Embodied Robots., , , , , , , and . Frontiers Robotics AI, (2016)Recombination Hotspots Promote the Evolvability of Modular Systems., , , , , , , , and . GECCO (Companion), page 115-116. ACM, (2016)